B000QJLQXU EBOK
Page 5
Let’s examine each of the component parts of the legend of development.
LEGEND PART ONE:
THE POOREST COUNTRIES ARE STUCK IN A
POVERTY TRAP FROM WHICH THEY CANNOT
EMERGE WITHOUT AN AID-FINANCED BIG PUSH.
The Big Push of massive aid flow was supposed to get poor countries out of what the UN Millennium Project calls a “poverty trap,” which automatically prevents very poor countries from growing. As Jeffrey Sachs explains it in his 2005 book, The End of Poverty, “When people are…utterly destitute, they need their entire income, or more, just to survive. There is no margin of income above survival that can be invested for the future. This is the main reason why the poorest of the poor are most prone to becoming trapped with low or negative economic growth rates. They are too poor to save for the future and thereby accumulate the capital that could pull them out of their current misery”
We can check this story out. As shown in table 1, we have data on per capita income from 1950 to 2001 for 137 countries, from a statistical compilation done by the economist Angus Maddison. (I exclude Communist economies and Persian Gulf oil producers as special cases.) We rank countries according to their per capita income in 1950. Did the poorest countries in 1950 remain stuck in poverty over the next half century? Well, no. The poorest fifth of countries in 1950 increased their income over the next five decades by a factor of 2.25. The other four fifths increased their incomes by a factor of 2.47. The difference in growth rates between the two groups is not statistically distinguishable from random fluctuation. We can statistically reject that the growth rate of the poorest countries as a group was zero. The only period that fits the legend is 1985–2001, to which I will return.
There are further statistical tests we can do to assess the poverty trap legend. If the legend holds, then the poorest countries should have stagnant income at a very low level. Income will fluctuate randomly around this level, but will always tend to return to it. There are two ways we can test whether there is a cursed stability of low income (known as “stationarity” in statistics jargon). We can assume stagnation and see whether the data reject that assumption, or we can assume instability of income—positive per capita growth is a nice form of instability—and see whether the data are consistent with that assumption (the data fail to reject instability). When we do a test for the stagnation of income over the subsequent half century for the poorest fifth of countries in 1950, we decisively reject the hypothesis of stagnation. When we assume instability—such as positive growth—the data provide no evidence against that assumption.
Perhaps it was aid that enabled poor countries to break out of stagnant income? When I break the sample in half into those poor countries that had above-average foreign aid and those that had below-average foreign aid, I find identical results for 1950–2001 in both halves as with the above tests of stable income. Over 1950–2001, countries with below-average aid had the same growth rate as countries with above-average foreign aid. Poor countries without aid had no trouble having positive growth.
This is a critical finding—the poorest countries can grow and develop on their own. Since foreign aid received does not explain these successes, perhaps they happened for entirely homegrown reasons. The Searchers among the poor can find a way toward higher living standards; they do not have to wait for the West to save them.
To be sure, among the poorest countries, there were individual poor countries that failed to grow. Chad had zero growth from 1950 to 2001. Zaire/Democratic Republic of the Congo (DRC) actually had negative per capita growth over this period. Aid still has a role to play to help those unlucky enough to be born into a stagnant economy—even if it doesn’t help the overall economy escape stagnation.
The stagnant economies were offset by such success stories as Botswana, which was the fourth poorest in 1950, but which increased its income by a factor of thirteen by 2001. Lesotho was the fifth poorest in 1950, but increased its income by a factor of five over the half century. Two other subsequent success stories who were among the poorest in 1950 are China and India.
Let us keep looking for confirmation of the two main predictions of the poverty trap legend: (1) that growth of the poorest countries is lower than other countries, and (2) that per capita growth of the poorest countries is zero or negative. The poorest did have lower growth than the others in an earlier period, 1950–1975. However, this was not a poverty trap, as average growth of the poorest during 1950–1975 was still a very healthy 1.9 percent per year (roughly the same as the long-run growth rate of the American economy, for example).
There is no evidence of lower growth for the poorest countries for recent periods, such as 1975–2001 or 1980–2001. Their growth was disappointing—much worse than in the previous period—but so was growth in middle-income countries. The poorest fifth of countries at the beginning of those periods had growth performance over the subsequent period that was statistically indistinguishable from the other four fifths of countries. Only when the starting point is put at 1985 does there finally appear evidence that the poorest did worse.
The evidence that Jeffrey Sachs adduces for the poverty trap in his book The End of Poverty is from this later period. So, from 1985 to the present, it is true that the poorest fifth of countries have had significantly lower per capita growth than other countries, about 1.1 percentage points lower. The next section will consider further whether this period fits the classic legend of the poverty trap.
The numbers in table 1 don’t seem to add up. The poorest countries did not have lower growth in the whole period 1950–2001, but they had slightly lower growth in 1950–1975, and much lower growth in more recent periods. The solution to the conundrum is that the identities of the poorest countries at the start of each period shown keeps changing. It doesn’t help the poverty trap legend that eleven out of the twenty-eight poorest countries in 1985 were not in the poorest fifth back in 1950. They got into poverty by declining from above, rather than from being stuck in it from below, while others escaped. If the identity of who is in the poverty trap keeps changing, then it must not be much of a trap.
Other scholars have also failed to find any evidence for a “poverty trap.1 One of the requirements for a poverty trap is the idea that saving is very low for poor people, increasing only at some intermediate level of income. Aart Kraay and Claudio Raddatz, in a January 2005 paper, studied the savings rate in all countries with data and found that that saving does not behave the way the poverty trap requires at low income. The reasons countries stay poor must lie elsewhere.
It is still possible that some countries are in a poverty trap; it is just that the average poor country is not. The theory of poverty traps is quite appealing: there are many ways in which we could think that countries are caught in traps. In a previous book, I give an example of how low average skills in the population could discourage new entrants to the labor force from getting skills, perpetuating a low-skill trap. Traps can also form at higher levels of income if there is some factor missing, such as high-quality formal institutions (which may itself be a consequence of insufficient income), keeping an economy stuck at a middle-income level.
With so many possible kinds of traps, it is not possible to definitively prove or refute the existence of traps in general. I can only test the specific form of the poverty trap discussed in the aid debates on the poorest countries, which predicts that being poor means a country will not grow without external assistance. This the data can reject.
LEGEND PART TWO:
WHENEVER POOR COUNTRIES HAVE LOUSY GROWTH,
IT IS BECAUSE OF A POVERTY TRAP RATHER
THAN BAD GOVERNMENT.
What about the period of lower growth and stagnation in poor countries in 1985–2001 shown in table 1? The UN Millennium Project argues that it is the poverty trap rather than bad government that explains the poor growth of those countries and their failure to make progress toward the Millennium Development Goals (MDGs). Jeffrey Sachs says, “The claim tha
t Africa’s corruption is the basic source of the problem [the poverty trap] does not withstand practical experience or serious scrutiny.2 Likewise the Millennium Project says, “Many reasonably well governed countries are too poor to make the investments to climb the first steps of the ladder.3
Why does it matter whether it is bad government or a technological poverty trap? The case for Planners is even weaker if they must deal with the complexities of bad government. (We will see in chapter 4 just how difficult it has been.) So aid advocates desperately want to disbelieve the bad government explanation for poverty, which is something akin to the church youth group minister who wants to believe that his charges are all virgins. Bad government is also bad for fund-raising for aid. Jeffrey Sachs worries in The End of Poverty: “If the poor are poor because…their governments are corrupt, how could global cooperation help?.4
Let us test bad government against the poverty trap as an explanation for poor economic growth. The earliest rating we have on corruption is from 1984, from the International Country Risk Guide. We have a rating on democracy for the same year from a research project at the University of Maryland called Polity IV. Let’s take countries that have the worst ratings on both corruption and democracy, and call these countries “bad governments.” While poor countries did worse, it’s also true that the twenty-four countries with bad governments in 1984 had significantly lower growth from 1985 to the present:1.3 percentage points slower than the rest. There is some overlap between these two stories, as poor countries are much more likely to have bad government. So which is it, bad government or the poverty trap? When we control both for initial poverty and for bad government, it is bad government that explains the slower growth. We cannot statistically discern any effect of initial poverty on subsequent growth once we control for bad government. This is still true if we limit the definition of bad government to corruption alone. The recent stagnation of the poorest countries appears to have more to do with awful government than with a poverty trap, contrary to the UN/Sachs hypothesis. Actually if those preparing the UN Millennium Project report about escaping the well-governed poverty trap had looked at the Millennium Project’s own country studies, they would have found interesting clues to this result, such as the following vignette on Cambodian schoolteachers: “Many supplement their income by soliciting bribes from students, including the sale of examination questions and answers….[T]he end result is a high dropout rate.5
There is another piece of evidence that we have to consider that looks like it does support the poverty trap story. Over the last two centuries, there has been a widening gap between rich and poor nations. This is what World Bank economist Lant Pritchett calls in a famous article “Divergence, Big Time.” There is historical data on about fifty countries from the economist Angus Maddison. The gap between the richest and poorest countries has widened drastically over the last two centuries, with the ratio of the max to the min going from about six to one two hundred years ago to about seventy to one today. There is a positive correlation between per capita growth from, say, 1820 to 2001 and the initial level of income in 1820.
Was this because the poor countries were stuck in a poverty trap? Well, first of all, the data do not fit our definition of a poverty trap—per capita growth of the poorest countries was not zero. The predicted level of annual per capita growth for the poorest countries in the sample in 1820 was 1.05 percent, with a margin of error of .25 percent. One limitation may be that African countries are not in the sample. However, Maddison gives an estimate for per capita income in the continent as a whole in 1820—per capita growth in Africa from 1820 to 2001 is 0.7 percent per annum, a 3.5-fold increase, not a poverty trap.
Still, let us consider the slower growth of the poorest countries as suggestive of a poverty trap. The alternative explanation to the “poverty trap” is that Europe and its offshoots had better government than the Rest. Good government could be correlated with per capita income in 1820, and that could explain why countries that were richer in 1820 subsequently grew faster. The poor countries were stuck with authoritarian governments (or another form of authoritarian rule: colonial occupation). This could imply a bad-government poverty trap, but not the savings-and-technology poverty trap favored by the UN/Sachs story.
I test this story by using again the data from the Polity IV research project, which covers democracy since 1820. I average whatever Polity data are available on each country over the period 1820–2001.6 It turns out that average democracy is significantly correlated with long-term growth in most specifications, and the positive relationship of growth with initial per capita income declines or even turns negative once you control for quality of government. The latter results would suggest that poor countries grow faster than rich countries if they have a good government (using democracy as a proxy for good government)—contrary to the Millennium Project idea that “many reasonably well governed countries are too poor to make the investments to climb the first steps of the ladder.” These results hold up when you control for possible reverse causality going from economic growth to bad government. There is no evidence that initially poor countries are at a disadvantage once you control for good government. The Big Push is not going to work if the problem is bad government rather than a poverty trap. We will see in chapter 4 what tortured conundrums foreign aid encounters when dealing with bad governments.
LEGEND PART THREE:
FOREIGN AID GIVES A BIG PUSH TO COUNTRIES TO
ACHIEVE A TAKEOFF INTO SELF-SUSTAINED GROWTH.
There is now a regular cycle in the literature on foreign aid and growth. Someone will survey the evidence and find that foreign aid does not produce growth. There will be some to-and-fro in the literature, in the course of which a few studies will find a positive effect of aid on growth. Foreign aid agencies will then seize upon the positive effect, usually focusing on only one study, and will publicize it widely. Researchers will examine the one positive result more carefully and find that it is spurious. Then there will be more to-and-fro in the literature, and a new twist will be discovered under which aid has a positive effect on growth. Aid agencies will seize on this result again, and the cycle will begin all over again.
We have already had a test of old and new theories of the Big Push in Africa. For a region as poor as Africa, aid receipts have already been large enough to constitute a Big Push. The typical African country received more than 15 percent of its income from foreign donors in the 1990s. Figure 2 shows the overall outcome for aid and growth in Africa. Aid accelerated as growth fell. Note that African growth over the previous ten years had been a respectable 2 percent up to about 1975 (with modest aid), contradicting the idea that Africa is always and everywhere condemned to low growth without aid. There is a negative association, but I don’t think the increase in aid caused the fall in growth. Rather, the fall in growth probably caused the increase in aid. But the surge of aid was not successful in reversing or halting the slide in growth of income per capita toward zero.
Let us do more formal statistical testing. Long and inconclusive literature on aid and economic growth was produced in the 1960s, 1970s, and 1980s, which was hampered by the limited data availability and inconclusive debate about the mechanisms by which aid would affect growth. The possible reverse causality made conclusions difficult: if donors gave greater aid in response to slower growth, then interpreting how aid flow affected growth could be difficult. The literature got new life in 1996 with a paper by London School of Economics economist Peter Boone, who found that aid financed consumption rather than investment. (Financing consumption of a few poor people is not so bad, but the Big Push hoped for the society-wide transformation that would come from aid financing investment and growth.) Boone addressed the problem of reverse causality by using political factors to predict which countries got aid—usually rich countries gave aid to poor countries that were their political allies, or with which they had a colonial association. When aid is predicted by political factors that are themselves unrelate
d to growth outcomes, you can examine whether the predicted values of aid caused higher growth. Even controlling for possible reverse causality, Boone found aid to have zero effect on investment. Similarly, controlling for reverse causality, he found aid to have zero effect on growth. The Economist publicized Boone’s research, and it was widely known in the aid policy-making community.
Fig. 2. Aid and Growth in Africa (ten-year moving averages)
Boone’s research created a terrible disjunction: aid policy was based on the premise that aid raises growth, but now the best study of the question was saying that this premise was false. Soon a study appeared to fill the vacuum between policy and research.7 an academic study by World Bank economists Craig Burnside and David Dollar.8 I am not saying that Burnside and Dollar consciously set out to reach a predetermined conclusion, which would obviously have been bad science. Rather, theirs was a serious scientific study; there were also other equally serious studies that found different results. The point is, the policy community chose to believe the finding that was most favorable to the aid policies it wanted to implement.